The saturation enhancement processing is one of the known image processing techniques applied to image data. In a display device which originally has a relatively narrow color gamut, for example, performing saturation enhancement processing on image data effectively compensates the color gamut in images actually displayed on the display screen. More specifically, a liquid crystal display device which uses a white backlight undesirably has a narrow color gamut compared to recent OLED (organic light emitting diode) display devices, and therefore an enlargement of the color gamut is often required to achieve beautiful coloring. Saturation enhancement processing to image data allows enlarging the effective color gamut to meet such a requirement.
Saturation enhancement processing is also used to recover the saturation, when the saturation of an image is deteriorated by an auto contrast optimization (ACO). In a typical auto contrast optimization, a contrast enhancement is achieved in response to characterization data obtained by analyzing image data (for example, the luminance histogram or the average picture level (APL) of the image); note that a contrast enhancement is disclosed in Japanese patent No. 4,198,720 B, for example. In a typical auto contrast optimization, however, the saturation, that is, the differences among the grayscale levels of the red color (R), the green color (G) and the blue color (B) may be reduced, because a common contrast enhancement is performed for the red, green and blue colors, as is understood from FIG. 1A. To address this problem, saturation enhancement processing is often performed on image data obtained by a contrast enhancement.
According to a study of the inventors, however, there is a room for improvement in known saturation enhancement techniques to achieve appropriate saturation enhancement with a reduced circuit size.
Such a situation is especially severe when different image processing (such as contrast enhancement) is performed in series with saturation enhancement processing. FIG. 1B illustrates an example of a system in which saturation enhancement processing is performed after contrast enhancement processing. In order to achieve an improved contrast enhancement, output image data obtained by the contrast enhancement processing needs to have a wider bit width than that of input image data, to avoid gradation collapse in the contrast enhancement processing. When input image data to be subjected to contrast enhancement processing represent the grayscale level of each of the red, green and blue colors with eight bits, for example, the output image data of the contrast enhancement processing may be generated as image data which represent the grayscale level of each of the red, green and blue colors with 10 bits. When the output image data of the contrast enhancement processing are further subjected to saturation enhancement processing, the image data obtained by the saturation enhancement processing need to have a further wider bit width. When the output image data of the contrast enhancement processing represent the grayscale level of each of the red, green and blue colors with 10 bits, for example, the output image data of the saturation enhancement processing may be generated as image data which represent the grayscale level of each of the red, green and blue colors with 12 bits. The increase in the bit widths of the input and output image data of the saturation enhancement processing, however, undesirably increases the size of the circuit used for the saturation enhancement processing.
As a technique potentially related to the present invention, Japanese Patent Application Publication No. 2010-79119 A discloses a technique in which red-green-blue (RGB) data are transformed into hue-saturation-value (HSV) data and the saturation enhancement is achieved in the HSV color space. Japanese Patent Application Publication No. H06-339017 A discloses a saturation enhancement in which the red (R), green (G), blue (B) values of a saturation-enhanced image are respectively calculated by subtracting products of an enhancement coefficient and the differences between I and the original R, G, and B values from I, where I is the maximum value of the R, G, and B values of each pixel.